The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
In agricultural modeling, it is often useful to compare various location based values with corresponding values at the locations. For example, predictions of yield may be generated for a plurality of sections of a field using agronomic modeling techniques. Comparing the predictions to measured yields may be useful for identifying errors in an agronomic model or for selecting agronomic models that led to more accurate predictions. Other examples of comparisons include comparisons of field related values, such as nutrient content in the soil, pH values, soil moisture, elevation, and temperature to measured yields.
Additionally, it is often useful to compare values across a plurality of years. For example, comparisons between measurements of yield over a plurality of years may be useful to determine consistency within a field which in turn may then inform management practices of the field. It is also useful to identify consistent spatial patterns within a field in order to identify portions of the field which act differently or produce different yields.
Conventional similarity metrics are based on differences of each value at each location. By comparing values individually, the conventional similarity metrics fail to take into account spatial relationships. For example, if each value shifted in one direction, a visual representation of the two sets of values would appear obviously similar while conventional metrics would treat the two data sets as extremely dissimilar.
The failure to account for spatial relationships is a shortcoming of metrics designed to compare sets of data. Spatial relationships can be extremely important in determining consistency of particular values or identifying correlations between particular values. Yet, given that data sets are treated as comprising discrete and independent data values, spatial relationships between nearby locations remain unaccounted for. Additionally, given the absence of spatial relationships, a comparison of individual data points to portions of an image, such as satellite images of a field, are unable to recognize the spatial relationships within the image.
Image comparison techniques take into account spatial relationship in two images, but they have their own shortcomings. First, image comparison techniques are generally only applicable to images, particularly images of similar resolution. Second, image comparison techniques tend to not be scalable, thereby limiting their usefulness in comparing similarity or difference metrics between two different sets of images of different sizes or shapes. Generally, two large images will produce a different range of similarity or difference metrics as two small images.
Thus, there is a need for techniques for generating images from discrete data sets, such as measured crop yields, such that two data sets may be compared using image comparison techniques. Additionally, there is a need for image comparison techniques that are scalable such that comparisons of a field of one size and shape can be measured against comparisons of a field of a second size and shape.